With the wide deployment of sensors and the emergence of the Internet of things (IoT), there arises a need for analysis of sensor data. Sensor data can be viewed as a time series i.e. a sequence of (time, value) pairs. Time series analysis includes statistical analysis such as linear regression, moving average and auto-regression. However, the time series analysis in the IoT platforms, require heavy investment in infrastructure. Often, to analyse a time series, the entire time series is loaded into memory, whether it is on one machine or on multiple distributed machines.
Current processing systems for autoregression require large amount of memory as, often, entire datasets are loaded into the memory. Such processing systems for autoregression are not optimized for running in an environment where a number of tasks are executed on the same machine, since each job has high memory overhead. Therefore, the systems may fail to process large chunks of data in the worker nodes, as the worker nodes typically tend to load the entire data of the partition in the memory, thereby resulting in system overhead and large memory consumption which leads to slow performance of machines.